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Corrections to “The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic”

IEEE Access 2025年 13卷 5436-5436页

作者： M. Poongodi Mohit Malviya Mounir Hamdi Hafiz Tayyab Rauf Seifedine Kadry Orawit Thinnukool College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar Department of CTO 5G Wipro Ltd. Bengaluru India Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent U.K. Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Research Group of Embedded Systems and Mobile Application in Health Science College of Arts Media and Technology Chiang Mai University Chiang Mai Thailand

Presents corrections to the paper, (Corrections to “The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic”).

关键词： COVID-19 Pandemics

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Preface

Advanced Structured Materials

引用

Advanced Structured Materials 2025年 228卷 v页

作者： Singh, Deepak Kumar Sambhav, Saurabh Pandey, Ashok Kumar Ismail, Azman Zulkipli, Fatin Nur Öchsner, Andreas Amity University Patna Bihar Patna India Employees Selection Board Bhopal Directorate of Technical Education RGPV Madhya Pradesh Bhopal India Maritime Engineering Technology and Centre for Women Advancement and Leadership Universiti Kuala Lumpur Malaysian Institute of Marine Engineering Technology Perak Lumut Malaysia Information Science Studies College of Computing Informatics and Mathematics Universiti Teknologi MARA Kelantan Machang Malaysia Faculty of Mechanical Engineering Esslingen University of Applied Sciences Baden-Württemberg Esslingen am Neckar Germany

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Reporting of Scope2 Co2-Emissions and Electricity Consumption in 111 Indian Ocean Cities between 2015-2022

SSRN

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SSRN 2024年

作者： Nematchoua, Modeste Kameni Buratti, Cinza Orosa, Jose A. Attia, Shady Teller, Jacques Bemanana, Muriel Akinola, Olatunji Bernard, Andrianirina Charles Sendrahasina, Rakotomalala Minoson Roy, Sambatra Eric Jean Falisoa, Rafanotsimiva Liva Jean-Pierre, Messina Reiter, Sigrid School of Informatics Computing and Cyber Systems Northern Arizona University 1295 S. Knoles Dr. Building 90 Room 320 FlagstaffAZ86011 United States Department of Engineering University of Perugia Via G. Duranti 63 Perugia06125 Italy Department of N. S. and Marine Engineering Escuela Técnica Superior de N. y M University of A Coruña Paseo de Ronda 51 A Coruna15011 Spain Sustainable Building Design Lab Dept. UEE Applied Sciences University of Liege Belgium LEMA ArGEnCo Department University of Liege Belgium Higher Polytechnic School University of Antsiranana Antsiranana Madagascar Science and Technology’s Higher Institute University of Mahajanga Mahajanga Madagascar University of Antananarivo Po Box 566 Antananarivo101 Madagascar Department of Industrial Engineering Higher Institute of Technology Antsiranana Antsiranana201 Madagascar Faculty of Architecture and Construction Notre-Dame du Kasaï University Congo

Several sources related that the electricity sector emits almost a quarter of greenhouse gases each year in the world. It is therefore one of the important sectors to take into account to limit global warming. Indian Ocean cities produce significant CO2 emissions during electricity consumption. Their volume and accuracy remain practically unknown and untested. Indeed, until now, there is no methodology suggested by the researchers to evaluate Fossil Fuel carbon dioxide (FFCO2) emission, and electricity consumption in this region. Aware of these crucial problems, this study was carried out to assess and analyse CO2 emissions coming from Electricity consumption (called Scope2) in 111 cities located in the Indian Ocean from 55 Power plants between 2015-2022 (08 years) and in four sectors (Residential, Commercial, Industrial, and On-road). To carry out a good comparison, all the data were grouped into three categories, Before COVID (2015-2018);During COVID (2019-2020);and After COVID (2021-2022)). The results showed that the CO2 emission difference is significant in the residential and commercial sectors. It was observed that CO2 emissions increased in 2019-2022 in the residential, industrial, and on-road sectors whereas, simultaneously during the same period, it decreased in the commercial sector. During the three periods, the CO2 emissions rate was the highest in the residential sector (around 52%), and the least on-road (around 1%). The significant difference in the commercial sector suggests a decrease in electricity consumption during the peak of the pandemic due to reduced business activities. Businesses adapted to new operating conditions, such as reduced hours or enhanced energy efficiency measures, which also contributed to the change in consumption patterns. © 2024, The Authors. All rights reserved.

关键词： Low emission

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Kurdish Handwritten Character Recognition using Deep Learning Techniques

arXiv

引用

arXiv 2022年

作者： Ahmed, Rebin M. Rashid, Tarik A. Fattah, Polla Alsadoon, Abeer Bacanin, Nebojsa Mirjalili, Seyedali Vimal, S. Chhabra, Amit IT Department Faculty of Applied Science Tishk International University Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Software and Informatics Engineering Salahaddin University-Erbil- Erbil Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Sydney Australia Singidunum University Belgrade Serbia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India

Handwriting recognition is one of the active and challenging areas of research in the field of image processing and pattern recognition. It has many applications that include: a reading aid for visual impairment, automated reading and processing for bank checks, making any handwritten document searchable, and converting them into structural text form, etc. Moreover, high accuracy rates have been recorded by handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. Yet there is no such system available for offline Kurdish handwriting recognition. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic\Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive dataset was created for handwritten Kurdish characters, which contains more than 40 thousand images. The created dataset has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system, the experimental results show an acceptable recognition level. The testing results reported a 96% accuracy rate, and training accuracy reported a 97% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and is comparable to the similar model of other languages' handwriting recognition systems. © 2022, CC BY.

关键词： Convolutional neural networks

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Stagnation point flow of g-Jitter nanofluid induced by natural convection with Newtonian heating thermal condition

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AIP Conference Proceedings 2024年第1期2895卷

作者： Mohamad Hidayad Ahmad Kamal Anati Ali Noraihan Afiqah Rawi Lim Yeou Jiann Sharidan Shafie Rahimah Mahat Abdul Rahman Mohd Kasim Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia 81310 UTM Johor Bahru Johor Bahru Johor Darul Takzim Malaysia Faculty of Computer and Mathematical Science UiTM Cawangan Negeri Sembilan Kampus Seremban 70300 Seremban Negeri Sembilan Malaysia Universiti Kuala Lumpur Malaysian Institute of Industrial Technology Persiaran Sinaran Ilmu Bandar Seri Alam 81750 Johor Bahru Malaysia Centre for Mathematical Sciences College of Computing and Applied Sciences Universiti Malaysia Pahang Kuantan Pahang Malaysia

Understanding engine lubrication, electronic device cooling systems, and chemical industry production processes require studying boundary layer flow with Newtonian heating boundary conditions with varied consequences. The optimal performance of practical applications necessitates the proper conditions where many practical engineering applications using Newtonian heating in their theoretical study, such as in heat absorption in solar radiation, and conjugate heat transference around fins. As a result, theoretical research on free convection flow at a stagnation point region subjected to time dependent was examined utilizing mathematical modeling of the fluid system guided by physical laws and principles. The effects considered and fluid system configuration were also emphasized in developing the mathematical model. The study aimed to investigate the impacts included in the flow problem when subjected to Newtonian boundary conditions mathematically. The fluid problem’s system of partial differential equations was simplified using a semi-similar transformation, and it was then numerically solved using the Keller-box approach. The effects considered were critically analyzed in terms of profiles and physical quantities to understand the fluid behavior and thermal characteristics. The results revealed that adding nanoparticles to the fluid system improved the thermal characteristics of the fluid system by increasing the Nusselt number (Nu). Skin friction coefficient increases along with the nanoparticle volume fraction parameter. The skin friction coefficient increases as a result of increased friction at the boundary body’s surface caused by the nanoparticle silt.

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Correction: Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment

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Journal of Cloud computing 2023年第1期12卷 1-1页

作者： Dalal, Surjeet Manoharan, Poongodi Kumar, Lilhore Umesh Seth, Bijeta alsekait, Deema Mohammed Simaiya, Sarita Hamdi, Mounir Raahemifar, Kaamran Department of Computer Science and Engineering Amity University Haryana Gurugram India Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar School of Computing University of Louisiana Lafayette USA Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science and Engineering B. M. Institute of Engineering & Technology Sonipat India Department of Computer Science and Information Technology Applied College Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia College of Information Sciences and Technology Data Science and Artificial Intelligence Program State College USA School of Optometry and Vision Science Faculty of Science University of Waterloo Waterloo Canada Faculty of Engineering University of Waterloo Waterloo Canada

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An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering

引用

Array 2025年 26卷

作者： Abualigah, Laith Alomari, Saleh Ali Almomani, Mohammad H. Zitar, Raed Abu Migdady, Hazem Saleem, Kashif Smerat, Aseel Snasel, Vaclav Ezugwu, Absalom E. Computer Science Department Al al-Bayt University Mafraq25113 Jordan Faculty of Science and Information Technology Jadara University Irbid21110 Jordan Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates CSMIS Department Oman College of Management and Technology Barka320 Oman Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh11362 Saudi Arabia Faculty of Educational Sciences Al-Ahliyya Amman University Amman19328 Jordan Computer Technologies Engineering Mazaya University College Nasiriyah Iraq Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Poruba-Ostrava 70800 Czech Republic Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom2520 South Africa

Data clustering plays a crucial role in various domains, such as image processing, pattern recognition, and data mining. Traditional clustering techniques often suffer from limitations like sensitivity to initialization, poor convergence, and entrapment in local optima. To address these challenges, this paper proposes an Enhanced Walrus Optimizer (IWO) tailored for clustering tasks. The proposed IWO integrates two powerful strategies–Opposition-Based Learning (OBL) and Mutation Search Strategy (MSS)–to improve population diversity and prevent premature convergence, thereby enhancing both exploration and exploitation capabilities. These enhancements enable more accurate and stable identification of cluster centers. The effectiveness of IWO is validated through extensive experiments on multiple benchmark clustering datasets and compared against several state-of-the-art metaheuristic algorithms, including PSO, GWO, AOA, and others. The results demonstrate that IWO achieves better results, indicating improved compactness and separation of clusters. Statistical validation using p-values and ranking scores further confirms the superiority of the proposed method. These findings suggest that IWO offers a robust and flexible framework for solving complex clustering problems. Future work will explore hybrid deep learning-integrated models and parallel implementations to enhance scalability. © 2025

关键词： Swarm intelligence

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Two-loop non-planar hexa-box integrals with one massive leg

arXiv

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arXiv 2022年

作者： Kardos, Adam Papadopoulos, Costas G. Smirnov, Alexander V. Syrrakos, Nikolaos Wever, Christopher University of Debrecen Faculty of Science and Technology Department of Experimental Physics PO Box 105 Debrecen4010 Hungary Institute of Nuclear and Particle Physics NCSR Demokritos Patr. Grigoriou E’ & 27 Neapoleos Str Agia Paraskevi15341 Greece Research Computing Center Moscow State University Moscow119991 Russia Moscow Center for Fundamental and Applied Mathematics Moscow119992 Russia Physik-Department Technische Universität München James-Franck-Str. 1 Garching85748 Germany Physics Division National Technical University of Athens Zografou Campus Athens15780 Greece

Based on the Simplified Differential Equations approach, we present results for the two-loop non-planar hexa-box families of master integrals. We introduce a new approach to obtain the boundary terms and establish a one-dimensional integral representation of the master integrals in terms of Generalised Polylogarithms, when the alphabet contains non-factorisable square roots. The results are relevant to the study of NNLO QCD corrections for W, Z and Higgs-boson production in association with two hadronic jets. Copyright © 2022, The Authors. All rights reserved.

关键词： Bosons

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Preface

Advanced Structured Materials

引用

Advanced Structured Materials 2023年 193卷 v页

作者： Ismail, Azman Zulkipli, Fatin Nur Yaakup, Syajaratunnur Öchsner, Andreas Centre for Women Advancement and Leadership Malaysian Institute of Marine Engineering Technology Universiti Kuala Lumpur Perak Lumut Malaysia Information Science Studies College of Computing Informatics and Mathematics Universiti Teknologi MARA Kelantan Branch Machang Malaysia Malaysian Institute of Marine Engineering Technology Universiti Kuala Lumpur Perak Lumut Malaysia Faculty of Mechanical Engineering Esslingen University Applied Sciences Esslingen am Neckar Germany

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EEG Source Localization Using Spatio-Temporal Neural Network

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China Communications 2019年第7期16卷 131-143页

作者： Song Cui Lijuan Duan Bei Gong Yuanhua Qiao Fan Xu Juncheng Chen Changming Wang Faculty of Information Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing 100124 China College of Applied Sciences Beijing University of Technology Beijing 100124 China The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing 100088 China Advanced Innovation Center for Human Brain Protection Capital Medical University Beijing 100069 China

Source localization of focal electrical activity from scalp electroencephalogram (sEEG) signal is generally modeled as an inverse problem that is highly ill-posed. In this paper, a novel source localization method is proposed to model the EEG inverse problem using spatio-temporal long-short term memory recurrent neural networks (LSTM). The network model consists of two parts, sEEG encoding and source decoding, to model the sEEG signal and receive the regression of source location. As there does not exist enough annotated sEEG signals correspond to specific source locations, simulated data is generated with forward model using finite element method (FEM) to act as a part of training signals. A framework for source localization is proposed to estimate the source position based on simulated training data. Experiments are done on simulated testing data. The results on simulated data exhibit good robustness on noise signal, and the proposed network solves the EEG inverse problem with spatio-temporal deep network. The result show that the proposed method overcomes the highly ill-posed linear inverse problem with data driven learning.

关键词： electroencephalogram LSTM source localization spatio-temporal modeling

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